南开大学机器人与信息自动化研究所
Institute of Robotics and Automatic Information System
2012年秋季先进机器人与MEMS技术系列学术讲座
Seminar Series：Advanced Robotics & MEMS

题目1：Coordination of Multi-Agent Systems with Limited Communication Data Rate.
报告人：李韬 博士
单位：中科院系统所

题目2：Consensus of Linear Multi-Agent Systems
报告人：程龙 博士
单位：中科院自动化所

时间：本周五 (12月14日) 下午2:30-4:30
地点：主楼227室

Abstract1： Communication data rate and energy constraints are important factors which have to be considered when investigating distributed coordination of multi-agent networks. Although many proposed average-consensus protocols are available, a fundamental theoretic problem remains open, namely, how many bits of information are necessary for each pair of adjacent agents to exchange at each time step to ensure average consensus? In this talk, we consider average-consensus of undirected networks of discrete-time first-order agents under communication constraints. Each agent has a real-valued state but can only exchange symbolic data with its neighbours. A distributed protocol is proposed based on dynamic encoding and decoding. It is proved that under the protocol designed, for a connected network, average-consensus can be achieved with an exponential convergence rate based on merely one bit information exchange between each pair of adjacent agents at each time step. An explicit form of the asymptotic convergence rate is given. It is shown that as the number of agents increases, the asymptotic convergence rate is related to the number of nodes, the communication data rate and the ratio of the algebraic connectivity to the spectral radius of the Laplacian of the communication graph. We also give a performance index to characterize the total communication energy to achieve average-consensus and show that the minimization of the communication energy leads to a trade-off between the convergence rate and the number of quantization levels. For the case with switching topology, we develop an adaptive scheme to select the numbers of quantization levels according to whether the associated channel is active or not. We prove that if the network is jointly connected, then under the protocol designed, average-consensus can be asymptotically achieved, and the convergence rate is quantified.

Abstract2: In the past decade, cooperative control of multi-agent systems has attracted increasing attention from both robotics and control communities. Among these cooperative control problems, consensus of multi-agent systems has become one focal topic due to its central role in the distributed coordination. This talk briefly introduces some of on-going research activities regarding the consensus problem in our group. Different from the well-studied first-order/second-order integral agents, the agent considered here is described by the continuous-time/discrete-time linear time-invariant dynamics, which results in the so-called linear multi-agent system. We study the consensus of this linear multi-agent system under fixed or switching communication topologies. Some sufficient and/or necessary conditions for ensuring consensus are presented. Furthermore, communication constraints like noises are considered, stochastic-approximation type protocols are proposed to attenuate the noise’s effect and the consensus can be reached in the stochastic sense (in mean square sense or in almost sure sense). Finally, some preliminary results on the consensus of nonlinear multi-agent systems (multi-manipulator systems) are to be reported.

李韬博士简介：
Dr. Tao Li was born in Tianjin, China, in September, 1981. He received his Bachelor"s Degree in Automation from Nankai University, Tianjin, China, in 2004, and the Ph.D. degree in Systems Theory from Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences (CAS), Beijing, China, in 2009. From 2008 to 2011, he was with School of Electrical and Electronic Engineering, Nanyang Technology University, Singapore, as a Project officer and a Research Fellow, successively. From September 2010 to January 2011, he was a Visiting Fellow of Australian National University. Since July 2009, he has been a faculty member of AMSS, CAS, where now he is an Assistant Professor.
His current research interests include stochastic systems, networked control, multi-agent systems and sensor networks. He was mentioned as one of the five finalists for the Young Author Prize of the 17th
IFAC World Congress, 2008. He received the Special President Prize of Chinese Academy of Sciences in 2009, the Best Paper Award of the 7th Asian Control Conference with coauthors in 2009, the 2009 Singapore Millennium Foundation Research Fellowship, and the 2010 Endeavour Research Fellowship Award from Australian government. He is a member of the Youth Innovation Promotion Association, CAS. He is currently an editorial board member of Mathematical Problems in Engineering. Since 2010, he has been a program committee member of Chinese Control Conference.

程龙博士简介：
Long Cheng received his B. Eng. in Control Engineering from NanKai University, Tianjin, China, in 2004. He received his Ph.D. degree in Control Theory and Control Engineering from Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2009 (graduated with honor).
Since July 2009, he joined the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, where he currently holds an Associate Professor position. From Feb. 2010 to Sept. 2010, he worked as a Postdoctoral Research Fellow in Department of Mechanical Engineering, College of Engineering, University of Saskatchewen, Saskatoon, Saskatchewen, Canada. From Sept. 2010 to Mar. 2011, he worked as a Postdoctoral Research Fellow in Department of Mechanical and Industrial Engineering, College of Engineering, Northeastern University, Boston, MA, USA.
Dr. Cheng has published more than 40 technical papers in peer-refereed journals and prestigious conference proceedings. He received the Special Prize of Presidential Scholarship awarded by Chinese Academy of Sciences and his doctoral thesis was nominated as National One-Hundred Distinguished Doctoral Dissertation. He serves as an editorial board member of two international journals “Neurocomputing” and “Neural Computing & Applications”. His current research interests include multi-agent system, neural networks, nonlinear control, and their applications to robotics.